


from __future__ import annotations

from typing import TypedDict







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       str
			_UpperCamelCase		:       int



def 							UpperCamelCase_(					_A :str	)->		list[str]:
			if not isinstance(_A					,  _A	):
						raise TypeError("The parameter s type must be str."	)

			return [s[i:] + s[:i] for i in range(len(_A	)	)]
def 							UpperCamelCase_(					_A :str	)->		BWTTransformDict:
			if not isinstance(_A					,  _A	):
						raise TypeError("The parameter s type must be str."	)
			if not s:
						raise ValueError("The parameter s must not be empty."	)

			UpperCamelCase__       =							all_rotations(_A	)
			rotations.sort()  # sort the list of rotations in alphabetically order
			# make a string composed of the last char of each rotation
			UpperCamelCase__       =							{
			    "bwt_string": "".join([word[-1] for word in rotations]	),
			    "idx_original_string": rotations.index(_A	),
			}
			return response
def 							UpperCamelCase_(					_A :str					,  _A :int	)->		str:
			if not isinstance(_A					,  _A	):
						raise TypeError("The parameter bwt_string type must be str."	)
			if not bwt_string:
						raise ValueError("The parameter bwt_string must not be empty."	)
			try:
						UpperCamelCase__       =							int(_A	)
			except ValueError:
						raise TypeError(
						    "The parameter idx_original_string type must be int or passive"
						    " of cast to int."	)
			if idx_original_string < 0:
						raise ValueError("The parameter idx_original_string must not be lower than 0."	)
			if idx_original_string >= len(_A	):
						raise ValueError(
						    "The parameter idx_original_string must be lower than" " len(bwt_string)."	)

			UpperCamelCase__       =							[""] * len(_A	)
			for _ in range(len(_A	)	):
						for i in range(len(_A	)	):
									UpperCamelCase__       =							bwt_string[i] + ordered_rotations[i]
						ordered_rotations.sort()
			return ordered_rotations[idx_original_string]


if __name__ == "__main__":
						__UpperCamelCase     		=							'Provide a string that I will generate its BWT transform: '
						__UpperCamelCase     		=							input(entry_msg).strip()
						__UpperCamelCase     		=							bwt_transform(s)
						print(
						    f'''Burrows Wheeler transform for string \'{s}\' results '''
						    f'''in \'{result["bwt_string"]}\''''
						)
						__UpperCamelCase     		=							reverse_bwt(result['bwt_string'], result['idx_original_string'])
						print(
						    f'''Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '''
						    f'''we get original string \'{original_string}\''''
						)






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       int 			=		(DDIMParallelScheduler,)
			_UpperCamelCase		:       List[Any] 			=		(('eta', 0.0), ('num_inference_steps', 50))




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							{
						    "num_train_timesteps": 1000,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "clip_sample": True,
						}

						config.update(**snake_case  )
						return config




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config(**snake_case  )
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__,   UpperCamelCase__       =							10, 0.0

						UpperCamelCase__       =							self.dummy_model()
						UpperCamelCase__       =							self.dummy_sample_deter

						scheduler.set_timesteps(snake_case  )

						for t in scheduler.timesteps:
									UpperCamelCase__       =							model(snake_case   ,    snake_case  )
									UpperCamelCase__       =							scheduler.step(snake_case   ,    snake_case   ,    snake_case   ,    snake_case  ).prev_sample

						return sample




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for timesteps in [100, 500, 1000]:
									self.check_over_configs(num_train_timesteps=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for steps_offset in [0, 1]:
									self.check_over_configs(steps_offset=snake_case  )

						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config(steps_offset=1  )
						UpperCamelCase__       =							scheduler_class(**snake_case  )
						scheduler.set_timesteps(5  )
						assert torch.equal(scheduler.timesteps   ,    torch.LongTensor([801, 601, 401, 201, 1]  )  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,    [0.002, 0.02, 0.2, 2]  ):
									self.check_over_configs(beta_start=snake_case   ,    beta_end=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for timestep_spacing in ["trailing", "leading"]:
									self.check_over_configs(timestep_spacing=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for rescale_betas_zero_snr in [True, False]:
									self.check_over_configs(rescale_betas_zero_snr=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self.check_over_configs(thresholding=snake_case  )
						for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(
												    thresholding=snake_case   ,    prediction_type=snake_case   ,    sample_max_value=snake_case   ,    )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t in [1, 10, 49]:
									self.check_over_forward(time_step=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t, num_inference_steps in zip([1, 10, 50]   ,    [10, 50, 500]  ):
									self.check_over_forward(time_step=snake_case   ,    num_inference_steps=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for t, eta in zip([1, 10, 49]   ,    [0.0, 0.5, 1.0]  ):
									self.check_over_forward(time_step=snake_case   ,    eta=snake_case  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						assert torch.sum(torch.abs(scheduler._get_variance(0   ,    0  ) - 0.0  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(420   ,    400  ) - 0.14771  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(980   ,    960  ) - 0.32460  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(0   ,    0  ) - 0.0  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487   ,    486  ) - 0.00979  )  ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999   ,    998  ) - 0.02  )  ) < 1E-5




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.scheduler_classes[0]
						UpperCamelCase__       =							self.get_scheduler_config()
						UpperCamelCase__       =							scheduler_class(**snake_case  )

						UpperCamelCase__,   UpperCamelCase__       =							10, 0.0
						scheduler.set_timesteps(snake_case  )

						UpperCamelCase__       =							self.dummy_model()
						UpperCamelCase__       =							self.dummy_sample_deter
						UpperCamelCase__       =							self.dummy_sample_deter + 0.1
						UpperCamelCase__       =							self.dummy_sample_deter - 0.1

						UpperCamelCase__       =							samplea.shape[0]
						UpperCamelCase__       =							torch.stack([samplea, samplea, samplea]   ,    dim=0  )
						UpperCamelCase__       =							torch.arange(snake_case  )[0:3, None].repeat(1   ,    snake_case  )

						UpperCamelCase__       =							model(samples.flatten(0   ,    1  )   ,    timesteps.flatten(0   ,    1  )  )
						UpperCamelCase__       =							scheduler.batch_step_no_noise(snake_case   ,    timesteps.flatten(0   ,    1  )   ,    samples.flatten(0   ,    1  )   ,    snake_case  )

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 1147.7904  ) < 1E-2
						assert abs(result_mean.item() - 0.4982  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop()

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 172.0067  ) < 1E-2
						assert abs(result_mean.item() - 0.223967  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(prediction_type="v_prediction"  )

						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 52.5302  ) < 1E-2
						assert abs(result_mean.item() - 0.0684  ) < 1E-3




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(set_alpha_to_one=snake_case   ,    beta_start=0.01  )
						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 149.8295  ) < 1E-2
						assert abs(result_mean.item() - 0.1951  ) < 1E-3







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.full_loop(set_alpha_to_one=snake_case   ,    beta_start=0.01  )
						UpperCamelCase__       =							torch.sum(torch.abs(snake_case  )  )
						UpperCamelCase__       =							torch.mean(torch.abs(snake_case  )  )

						assert abs(result_sum.item() - 149.0784  ) < 1E-2
						assert abs(result_mean.item() - 0.1941  ) < 1E-3


